Unipower offers machine learning based power quality analysis in collaboration with the Chalmers startup Eneryield

Through a collaboration with the Chalmers startup Eneryield, which develops machine learning based methods for intelligent power quality analysis, Unipower will be offering a new type of analytics report to give insights in the work within power quality. Ebrahim Balouji and Karl Bäckström are the Chalmers researchers who have developed the technology.

The product enables new functionalities for identifying root cause and direction of power quality disturbances. The generated information enables more efficient actions of disturbances and requires less manual work. It is an important step towards a smarter power grid and creates conditions for better grid stability and security of supply.

Eneryield’s technology is based on the latest research within AI and deep neural networks, creating new opportunities for analytics. The large amount of data that Unipower has access to enables data-driven and self-learning methods that will set a new level for the kind of information that can be collected from the power grid. As the collaboration is extended, the machine learning functionality will be expanded.

Unipower will offer the product as a report that can be generated in PQ Secure.